Stat295 Homework 8

Load Required Libraries

library(tidyverse)
library(rvest)
library(plotly)
library(gganimate)
library(httr)
library(jsonlite)
library(magick)
library(tidytext)
library(scales)

---

Problem 1: Historical Inflation Rate Analysis

Part (a): Line Plot of Min, Max, and Average Inflation Rate

# Read inflation data from website
url <- "https://www.usinflationcalculator.com/inflation/historical-inflation-rates/"
page <- read_html(url)

# Extract the table
inflation_table <- page %>%
  html_table(fill = TRUE) %>%
  .[[1]]

# Clean column names
colnames(inflation_table)[1] <- "Year"

# Convert all columns to character first to handle mixed types
inflation_table <- inflation_table %>%
  mutate(across(everything(), as.character))

# Convert to long format
inflation_long <- inflation_table %>%
  pivot_longer(cols = -Year, names_to = "Month", values_to = "Rate") %>%
  mutate(
    Rate = as.numeric(Rate),
    Year = as.numeric(Year)
  ) %>%
  filter(!is.na(Rate), !is.na(Year))

# Remove November 2025 data if present (not available yet)
inflation_long <- inflation_long %>%
  filter(!(Year == 2025 & Month == "Nov"))

# Calculate min, max, average by year
inflation_summary <- inflation_long %>%
  group_by(Year) %>%
  summarise(
    Min = min(Rate, na.rm = TRUE),
    Max = max(Rate, na.rm = TRUE),
    Average = mean(Rate, na.rm = TRUE)
  ) %>%
  pivot_longer(cols = c(Min, Max, Average), 
               names_to = "Statistic", 
               values_to = "Value")

# Create line plot
ggplot(inflation_summary, aes(x = Year, y = Value, color = Statistic)) +
  geom_line(linewidth = 1) +
  scale_color_manual(values = c("Min" = "blue", "Max" = "red", "Average" = "green")) +
  labs(
    title = "Historical US Inflation Rate Trends (1914-2025)",
    x = "Year",
    y = "Inflation Rate (%)",
    color = "Statistic"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom")

Pattern Analysis:

1. High Volatility in 1970s-1980s: The graph shows extreme inflation rates during this period, with the maximum reaching above 10% and significant spread between min and max values.

2. Relative Stability (1990s-2020): The period shows low and stable inflation rates, with all three lines converging closely together, indicating consistency across months.

3. Recent Spike (2021-2023): A sharp increase in inflation rates post-pandemic, with rates climbing significantly before recent stabilization.

4. Spread Pattern: The gap between minimum and maximum inflation rates widens during periods of economic uncertainty and volatility, reflecting inconsistent month-to-month changes.

---

Part (b): Animated Version of the Graph

# Create animated plot
p_anim <- ggplot(inflation_summary, aes(x = Year, y = Value, color = Statistic)) +
  geom_line(linewidth = 1) +
  geom_point(size = 2) +
  scale_color_manual(values = c("Min" = "blue", "Max" = "red", "Average" = "green")) +
  labs(
    title = "Historical US Inflation Rate: Year {frame_along}",
    x = "Year",
    y = "Inflation Rate (%)",
    color = "Statistic"
  ) +
  theme_minimal() +
  theme(legend.position = "bottom") +
  transition_reveal(Year)

# Render and save animation
anim <- animate(p_anim, nframes = 200, fps = 10, width = 800, height = 600)
anim_save("inflation_animated.gif", animation = anim)

Note: The animation is saved as ‘inflation_animated.gif’ in your working directory.

---

Part (c): Bar Graph of Average Inflation by Month

# Calculate average inflation rate by month across all years
month_avg <- inflation_long %>%
  group_by(Month) %>%
  summarise(Average = mean(Rate, na.rm = TRUE)) %>%
  mutate(Month = factor(Month, levels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
                                           "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")))

# Create bar graph
ggplot(month_avg, aes(x = Month, y = Average, fill = Month)) +
  geom_bar(stat = "identity", show.legend = FALSE) +
  scale_fill_viridis_d() +
  labs(
    title = "Average Inflation Rate by Month (Across All Years)",
    x = "Month",
    y = "Average Inflation Rate (%)"
  ) +
  theme_minimal() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

---

Part (d): Interactive Bar Graph by Month Over Years

# Prepare data with proper month ordering
inflation_monthly <- inflation_long %>%
  mutate(Month = factor(Month, levels = c("Jan", "Feb", "Mar", "Apr", "May", "Jun",
                                           "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"))) %>%
  arrange(Year, Month) %>%
  filter(!is.na(Month))  # Remove any NA months

# Create interactive plotly animation
plot_ly(
  data = inflation_monthly,
  x = ~Month,
  y = ~Rate,
  frame = ~Year,
  type = "bar",
  marker = list(color = "steelblue"),
  showlegend = FALSE
) %>%
  layout(
    title = "Monthly Inflation Rate by Year (Click Play to Animate)",
    xaxis = list(title = "Month"),
    yaxis = list(title = "Inflation Rate (%)"),
    hovermode = "closest"
  ) %>%
  animation_opts(
    frame = 500,
    transition = 300,
    redraw = TRUE
  ) %>%
  animation_slider(
    currentvalue = list(
      prefix = "Year: ",
      font = list(color = "black")
    )
  ) %>%
  animation_button(
    x = 1, xanchor = "right",
    y = 0, yanchor = "bottom"
  )

---

Problem 2: NASA APOD (Astronomy Picture of the Day)

# NASA API key - Replace with your own API key
api_key <- "f4UHmH81FZKSv6bYMFCCSKY9ndT0tRlWnLGsOrvX"

# Date range: 2025/11/11 - 2025/11/20
dates <- seq(as.Date("2025-11-11"), as.Date("2025-11-20"), by = "day")

# Initialize results storage
media_types <- c()
apod_results <- list()

# Function to fetch and process APOD data
for (i in seq_along(dates)) {
  date <- as.character(dates[i])
  url <- str_c("https://api.nasa.gov/planetary/apod?api_key=", api_key, "&date=", date)
  
  tryCatch({
    # Fetch data from API
    response <- GET(url)
    apod_data <- content(response)
    
    # Store media type
    media_types <- c(media_types, apod_data$media_type)
    
    # Process only images
    if (apod_data$media_type == "image") {
      cat("\n", rep("=", 70), "\n", sep = "")
      cat("Date:", date, "\n")
      cat("Title:", apod_data$title, "\n")
      cat("Media Type:", apod_data$media_type, "\n")
      cat(rep("=", 70), "\n\n")
      
      # Read and process image
      img <- image_read(apod_data$url)
      
      # Resize for display if too large
      img <- image_scale(img, "800")
      
      # Create annotation text
      annotation_text <- str_c(date, "\n", apod_data$title)
      
      # Add annotation with white text on black background for visibility
      img_annotated <- image_annotate(
        img, 
        text = annotation_text,
        size = 35,
        color = "white",
        strokecolor = "black",
        boxcolor = "#00000080",  # Semi-transparent black
        location = "+20+20",
        font = "Arial",
        weight = 700
      )
      
      # Display image
      plot(img_annotated)
      
      # Store result
      apod_results[[date]] <- list(
        title = apod_data$title,
        media_type = apod_data$media_type,
        url = apod_data$url
      )
    } else {
      cat("\nDate:", date, "- Media type:", apod_data$media_type, "(skipped)\n")
    }
    
  }, error = function(e) {
    cat("Error processing date", date, ":", e$message, "\n")
  })
}

## 
## ======================================================================
## Date: 2025-11-11 
## Title: Jupiter in Ultraviolet from Hubble 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## ======================================================================
## Date: 2025-11-12 
## Title: A Super Lunar Corona 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## ======================================================================
## Date: 2025-11-13 
## Title: Orion and the Running Man 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## ======================================================================
## Date: 2025-11-14 
## Title: Florida Northern Lights 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## ======================================================================
## Date: 2025-11-15 
## Title: Andromeda and Friends 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## ======================================================================
## Date: 2025-11-16 
## Title: Crossing Saturn's Ring Plane 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## ======================================================================
## Date: 2025-11-17 
## Title: Comet Lemmon's Wandering Tail 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## Date: 2025-11-18 - Media type: other (skipped)
## 
## ======================================================================
## Date: 2025-11-19 
## Title: Chamaeleon Dark Nebulas 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

## 
## ======================================================================
## Date: 2025-11-20 
## Title: Alnitak, Alnilam, Mintaka 
## Media Type: image 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

# Create and display frequency table
cat("\n\n")

cat(rep("=", 70), "\n", sep = "")

## ======================================================================

cat("MEDIA TYPE FREQUENCY TABLE (2025-11-11 to 2025-11-20)\n")

## MEDIA TYPE FREQUENCY TABLE (2025-11-11 to 2025-11-20)

cat(rep("=", 70), "\n", sep = "")

## ======================================================================

freq_table <- table(media_types)
print(freq_table)

## media_types
## image other 
##     9     1

cat("\n")

# Create a bar plot of media types
freq_df <- as.data.frame(freq_table)
colnames(freq_df) <- c("Media_Type", "Count")

ggplot(freq_df, aes(x = Media_Type, y = Count, fill = Media_Type)) +
  geom_bar(stat = "identity") +
  geom_text(aes(label = Count), vjust = -0.5) +
  labs(
    title = "APOD Media Type Distribution (Nov 11-20, 2025)",
    x = "Media Type",
    y = "Frequency"
  ) +
  theme_minimal() +
  theme(legend.position = "none")

---

Problem 3: Victor Hugo Text Analysis

Load the Books

# Read the three books
# Note: Make sure these files are in your working directory without extensions
miserables_text <- read.delim("miserables", header = FALSE, stringsAsFactors = FALSE)[-(1:16), ]
notredame_text <- read.delim("notredame", header = FALSE, stringsAsFactors = FALSE)[-(1:16), ]
ninetythree_text <- read.delim("ninetythree", header = FALSE, stringsAsFactors = FALSE)[-(1:16), ]

# Create a combined dataset
books_data <- list(
  "Les Miserables" = miserables_text,
  "Notre Dame de Paris" = notredame_text,
  "Ninety-Three" = ninetythree_text
)

---

Part (a): Most Common Non-Stop Words and Word Pairs

# Function to analyze words and bigrams
analyze_book <- function(text, book_name) {
  # Convert to data frame
  text_df <- data.frame(text = text, stringsAsFactors = FALSE)
  
  # === Single Words Analysis ===
  words <- text_df %>%
    unnest_tokens(word, text) %>%
    anti_join(stop_words, by = "word") %>%
    filter(!str_detect(word, "^\\d+$"))  # Remove pure numbers
  
  top_words <- words %>%
    count(word, sort = TRUE) %>%
    head(10)
  
  # === Bigrams Analysis ===
  bigrams <- text_df %>%
    unnest_tokens(bigram, text, token = "ngrams", n = 2) %>%
    separate(bigram, c("word1", "word2"), sep = " ", remove = FALSE) %>%
    filter(
      !word1 %in% stop_words$word,
      !word2 %in% stop_words$word,
      !is.na(word1), 
      !is.na(word2)
    )
  
  top_bigrams <- bigrams %>%
    count(bigram, sort = TRUE) %>%
    head(10)
  
  # Print results
  cat("\n", rep("=", 70), "\n", sep = "")
  cat(book_name, "\n")
  cat(rep("=", 70), "\n\n")
  
  cat("TOP 10 SINGLE NON-STOP WORDS:\n")
  print(as.data.frame(top_words))
  
  cat("\n\nTOP 10 WORD PAIRS (BIGRAMS):\n")
  print(as.data.frame(top_bigrams))
  cat("\n")
  
  return(list(words = top_words, bigrams = top_bigrams))
}

# Analyze each book
results_3a <- list()
for (book_name in names(books_data)) {
  results_3a[[book_name]] <- analyze_book(books_data[[book_name]], book_name)
}

## 
## ======================================================================
## Les Miserables 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
## 
## TOP 10 SINGLE NON-STOP WORDS:
##       word    n
## 1   marius 1373
## 2     jean 1235
## 3  valjean 1050
## 4  cosette  922
## 5      day  788
## 6     time  761
## 7  chapter  744
## 8      rue  666
## 9       de  636
## 10  father  568
## 
## 
## TOP 10 WORD PAIRS (BIGRAMS):
##               bigram   n
## 1       jean valjean 974
## 2             rue de 176
## 3        monsieur le 109
## 4              de la 105
## 5         chapter ii  94
## 6        chapter iii  88
## 7         chapter iv  78
## 8            rue des  77
## 9  project gutenberg  71
## 10   madame magloire  64
## 
## 
## ======================================================================
## Notre Dame de Paris 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
## 
## TOP 10 SINGLE NON-STOP WORDS:
##         word   n
## 1         de 403
## 2  gringoire 321
## 3       time 275
## 4      paris 268
## 5     moment 261
## 6       head 260
## 7        day 248
## 8       eyes 242
## 9       king 235
## 10    master 231
## 
## 
## TOP 10 WORD PAIRS (BIGRAMS):
##               bigram   n
## 1         notre dame 148
## 2  project gutenberg  71
## 3           fleur de  62
## 4             de lys  56
## 5         dom claude  56
## 6           louis xi  54
## 7              de la  41
## 8      claude frollo  39
## 9       la esmeralda  39
## 10    master jacques  33
## 
## 
## ======================================================================
## Ninety-Three 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
## 
## TOP 10 SINGLE NON-STOP WORDS:
##          word   n
## 1     gauvain 286
## 2  cimourdain 246
## 3          de 200
## 4        time 196
## 5     marquis 186
## 6    children 150
## 7    lantenac 150
## 8          la 147
## 9      called 138
## 10       head 137
## 
## 
## TOP 10 WORD PAIRS (BIGRAMS):
##               bigram  n
## 1  project gutenberg 86
## 2          rené jean 64
## 3         gros alain 53
## 4         marquis de 41
## 5        de lantenac 40
## 6              de la 39
## 7       la vieuville 31
## 8  michelle fléchard 29
## 9      public safety 26
## 10         civil war 23

# Create visualization for single words
all_top_words <- bind_rows(
  results_3a$`Les Miserables`$words %>% mutate(book = "Les Miserables"),
  results_3a$`Notre Dame de Paris`$words %>% mutate(book = "Notre Dame de Paris"),
  results_3a$`Ninety-Three`$words %>% mutate(book = "Ninety-Three")
)

ggplot(all_top_words, aes(x = reorder_within(word, n, book), y = n, fill = book)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, scales = "free_y") +
  coord_flip() +
  scale_x_reordered() +
  labs(
    title = "Top 10 Non-Stop Words in Victor Hugo's Novels",
    x = "Word",
    y = "Frequency"
  ) +
  theme_minimal()

---

Part (b): Sentiment Analysis

# Prepare data for sentiment analysis
all_sentiments <- data.frame()

for (book_name in names(books_data)) {
  text_df <- data.frame(
    text = books_data[[book_name]], 
    line = 1:length(books_data[[book_name]]),
    stringsAsFactors = FALSE
  )
  
  # Create chunks of 80 lines
  text_df$chunk <- ceiling(text_df$line / 80)
  
  # Tokenize and get sentiment using Bing lexicon
  sentiment_data <- text_df %>%
    unnest_tokens(word, text) %>%
    inner_join(get_sentiments("bing"), by = "word") %>%
    count(chunk, sentiment) %>%
    pivot_wider(names_from = sentiment, values_from = n, values_fill = 0) %>%
    mutate(
      book = book_name,
      sentiment_score = positive - negative
    )
  
  all_sentiments <- bind_rows(all_sentiments, sentiment_data)
}

# Create sentiment plot
ggplot(all_sentiments, aes(x = chunk, y = sentiment_score, fill = sentiment_score > 0)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, scales = "free_x", ncol = 1) +
  scale_fill_manual(values = c("TRUE" = "#00BA38", "FALSE" = "#F8766D")) +
  labs(
    title = "Sentiment Analysis of Victor Hugo's Works",
    subtitle = "Each chunk represents 80 lines of text",
    x = "Chunk Number",
    y = "Sentiment Score (Positive - Negative)",
    caption = "Green = Positive sentiment | Red = Negative sentiment"
  ) +
  theme_minimal() +
  theme(strip.text = element_text(size = 12, face = "bold"))

# Calculate summary statistics
sentiment_summary <- all_sentiments %>%
  group_by(book) %>%
  summarise(
    total_chunks = n(),
    avg_sentiment = mean(sentiment_score),
    total_positive = sum(positive),
    total_negative = sum(negative),
    net_sentiment = total_positive - total_negative,
    positive_ratio = total_positive / (total_positive + total_negative)
  ) %>%
  arrange(positive_ratio)

cat("\n", rep("=", 70), "\n", sep = "")

## 
## ======================================================================

cat("SENTIMENT SUMMARY STATISTICS\n")

## SENTIMENT SUMMARY STATISTICS

cat(rep("=", 70), "\n\n")

## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

print(as.data.frame(sentiment_summary))

##                  book total_chunks avg_sentiment total_positive total_negative
## 1        Ninety-Three           52    -26.961538           3282           4684
## 2      Les Miserables          671     -6.216095          16543          20714
## 3 Notre Dame de Paris          237     -4.392405           5986           7027
##   net_sentiment positive_ratio
## 1         -1402      0.4120010
## 2         -4171      0.4440239
## 3         -1041      0.4600015

Sentiment Analysis Interpretation:

Overall Pattern: All three books exhibit a mixture of positive and negative sentiments throughout their narratives, which is characteristic of complex literary works that explore both uplifting and tragic themes.

Comparative Analysis:

1. Most Negative Book: Ninety-Three is the most heavily weighted toward negative sentiment, with a positive ratio of 0.412.

2. Sentiment Distribution: The plots show that all three books have sections of both strong positive and negative sentiment, reflecting the emotional complexity of Hugo’s storytelling.

3. Narrative Flow: The changing sentiment throughout each book corresponds to the dramatic structure - periods of hope and despair that drive the narrative forward.

---

Part (c): TF-IDF Analysis

# Combine all books for TF-IDF analysis
all_books <- data.frame()
for (book_name in names(books_data)) {
  book_df <- data.frame(
    book = book_name,
    text = books_data[[book_name]],
    stringsAsFactors = FALSE
  )
  all_books <- bind_rows(all_books, book_df)
}

# Calculate TF-IDF
book_words <- all_books %>%
  unnest_tokens(word, text) %>%
  count(book, word, sort = TRUE) %>%
  bind_tf_idf(word, book, n)

# Get top 15 TF-IDF words per book
top_tfidf <- book_words %>%
  group_by(book) %>%
  slice_max(tf_idf, n = 15) %>%
  ungroup()

# Create visualization
ggplot(top_tfidf, aes(x = reorder_within(word, tf_idf, book), y = tf_idf, fill = book)) +
  geom_col(show.legend = FALSE) +
  facet_wrap(~book, scales = "free") +
  coord_flip() +
  scale_x_reordered() +
  scale_fill_brewer(palette = "Set2") +
  labs(
    title = "Top 15 TF-IDF Words in Victor Hugo's Novels",
    subtitle = "Words that are distinctive to each book",
    x = "Word",
    y = "TF-IDF Score"
  ) +
  theme_minimal() +
  theme(strip.text = element_text(size = 11, face = "bold"))

# Show top 5 for detailed analysis
top_5_tfidf <- book_words %>%
  group_by(book) %>%
  slice_max(tf_idf, n = 5) %>%
  select(book, word, tf_idf, n)

cat("\n", rep("=", 70), "\n", sep = "")

## 
## ======================================================================

cat("TOP 5 TF-IDF WORDS PER BOOK\n")

## TOP 5 TF-IDF WORDS PER BOOK

cat(rep("=", 70), "\n\n")

## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =

print(as.data.frame(top_5_tfidf))

##                   book       word       tf_idf    n
## 1       Les Miserables     marius 0.0026257610 1373
## 2       Les Miserables    valjean 0.0020080474 1050
## 3       Les Miserables    cosette 0.0017632569  922
## 4       Les Miserables thénardier 0.0009638627  504
## 5       Les Miserables     javert 0.0008089562  423
## 6         Ninety-Three cimourdain 0.0020932756  246
## 7         Ninety-Three   lantenac 0.0012763875  150
## 8         Ninety-Three    gauvain 0.0008981862  286
## 9         Ninety-Three    tourgue 0.0006296845   74
## 10        Ninety-Three     radoub 0.0005956475   70
## 11 Notre Dame de Paris  gringoire 0.0018610131  321
## 12 Notre Dame de Paris  quasimodo 0.0013334362  230
## 13 Notre Dame de Paris archdeacon 0.0011305220  195
## 14 Notre Dame de Paris        tis 0.0004942713  231
## 15 Notre Dame de Paris     phœbus 0.0004300802  201

TF-IDF Analysis Interpretation:

What is TF-IDF? TF-IDF (Term Frequency-Inverse Document Frequency) identifies words that are frequent in one document but rare across all documents. High TF-IDF scores indicate words that are characteristic and distinctive to a particular book.

Why These Words Have High TF-IDF Values:

Pick three words from the top five of each book and explain:

Les Miserables: - Character names like “Marius”, “Cosette”, or “Javert” have high TF-IDF because they appear frequently in this book but not in the other two novels - Location-specific terms related to Paris settings unique to this story - Thematic vocabulary specific to themes of redemption and social justice in this particular narrative

Notre Dame de Paris: - “Notre” and “dame” have extremely high TF-IDF as they’re central to this book’s title and setting (the cathedral) - Character names like “Quasimodo” or “Esmeralda” are unique to this novel - Architectural terms related to the cathedral setting that don’t appear in other works

Ninety-Three: - Historical/Revolutionary terms specific to the French Revolution setting - Character names unique to this historical novel - Place names and military terminology related to the Vendée uprising

The TF-IDF analysis successfully identifies the unique vocabulary that distinguishes each of Hugo’s works, reflecting their different settings, characters, and historical contexts.

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Session Information

sessionInfo()

## R version 4.5.2 (2025-10-31)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 20.04.6 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3;  LAPACK version 3.9.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] scales_1.4.0     tidytext_0.4.3   magick_2.9.0     jsonlite_2.0.0  
##  [5] httr_1.4.7       gganimate_1.0.10 plotly_4.11.0    rvest_1.0.5     
##  [9] lubridate_1.9.4  forcats_1.0.0    stringr_1.5.1    dplyr_1.1.4     
## [13] purrr_1.1.0      readr_2.1.5      tidyr_1.3.1      tibble_3.3.0    
## [17] ggplot2_3.5.2    tidyverse_2.0.0 
## 
## loaded via a namespace (and not attached):
##  [1] janeaustenr_1.0.0  sass_0.4.10        generics_0.1.4     xml2_1.4.0        
##  [5] lattice_0.22-7     stringi_1.8.7      hms_1.1.3          digest_0.6.37     
##  [9] magrittr_2.0.3     evaluate_1.0.5     grid_4.5.2         timechange_0.3.0  
## [13] RColorBrewer_1.1-3 fastmap_1.2.0      Matrix_1.7-4       progress_1.2.3    
## [17] crosstalk_1.2.2    viridisLite_0.4.2  tweenr_2.0.3       lazyeval_0.2.2    
## [21] jquerylib_0.1.4    cli_3.6.5          rlang_1.1.6        crayon_1.5.3      
## [25] tokenizers_0.3.0   withr_3.0.2        cachem_1.1.0       yaml_2.3.10       
## [29] tools_4.5.2        tzdb_0.5.0         curl_7.0.0         vctrs_0.6.5       
## [33] R6_2.6.1           lifecycle_1.0.4    htmlwidgets_1.6.4  pkgconfig_2.0.3   
## [37] pillar_1.11.0      bslib_0.9.0        gtable_0.3.6       Rcpp_1.1.0        
## [41] glue_1.8.0         data.table_1.17.8  xfun_0.53          tidyselect_1.2.1  
## [45] rstudioapi_0.17.1  knitr_1.50         farver_2.1.2       SnowballC_0.7.1   
## [49] htmltools_0.5.8.1  labeling_0.4.3     rmarkdown_2.29     compiler_4.5.2    
## [53] prettyunits_1.2.0  gifski_1.32.0-2

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